code: def inverse_transform(self, X):
      if self._old_column is None:
          return X
  
      old_col = X[self._new_columns[0]] * 0
  
      for unique_value in self._uniq_values:
          new_col = f"{self._old_column}_{unique_value}"
          if new_col not in self._new_columns:
              old_col[:] = unique_value
          else:
              old_col[X[new_col] == 1] = unique_value
  
      X[self._old_column] = old_col
      X.drop(self._new_columns, axis=1, inplace=True)
      return X
  
file_path: mljar-supervised/supervised/preprocessing/label_binarizer.py
imports: import json
  import numpy
label_desc: Use image node_id 4 for calling the LabelBinarizer obj's underlying member method code with example usage: obj.inverse_transform(X) and returns: X, X